Given the pace at which human-induced environmental changes occur, a pressing challenge is to determine the speed with which selection can drive evolutionary change. A key determinant of adaptive response to multivariate phenotypic selection is the additive genetic variance–covariance matrix (G). Yet knowledge of G in a population experiencing new or altered selection is not sufficient to predict selection response because G itself evolves in ways that are poorly understood. We experimentally evaluated changes in G when closely related behavioural traits experience continuous directional selection. We applied the genetic covariance tensor approach to a large dataset (n = 17 328 individuals) from a replicated, 31-generation artificial selection experiment that bred mice for voluntary wheel running on days 5 and 6 of a 6-day test. Selection on this subset of G induced proportional changes across the matrix for all 6 days of running behaviour within the first four generations. The changes in G induced by selection resulted in a fourfold slower-than-predicted rate of response to selection. Thus, selection exacerbated constraints within G and limited future adaptive response, a phenomenon that could have profound consequences for populations facing rapid environmental change.
Careau_et_al_G-matrix_DATA_DRYAD
Careau_et_al_G-matrix_PEDIGREE_DRYAD
Careau_et_al_G-matrix_CODE_TENSOR_SIMULATION
TensorExampleMod_pop1a
TensorExampleMod_pop1b
TensorExampleMod_pop1c
TensorExampleMod_pop2a
TensorExampleMod_pop1d
TensorExampleMod_pop2b
TensorExampleMod_pop2c
TensorExampleMod_pop2d
This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license.
- Artificial selection
- Bayesian animal model
- Experimental evolution
- G-matrix
- genetic covariance tensor
- Mus musculus
- wheel running